<![CDATA[This study is motivated by the increasing use of transaction data as a strategic information source in business decision-making, particularly in the sale of vehicle parts. However, large and complex transaction datasets are often not fully utilized to identify consumer purchasing patterns. Therefore, this study aims to identify association patterns between products using the Apriori algorithm, thereby providing recommendations to support marketing strategies, inventory management, and sales growth. The research method employed is a quantitative approach based on data mining using the Association Rule Mining (ARM) technique. The research stages include transaction data collection, preprocessing (cleaning, transformation, and conversion to transaction format), itemset formation, and the application of the Apriori algorithm using a minimum support parameter of 0.5% and a confidence level of 60%. The analyzed data consists of 10,922 transactions divided into training and testing datasets. The results of the study indicate that the Apriori algorithm is capable of generating association rules with high confidence values of up to 83%, indicating strong relationships between items. Specific items such as 9-09060-EXCHEM emerge as the central item in various rules, demonstrating a dominant role in purchasing patterns. These findings prove that the Apriori approach is effective in uncovering purchasing patterns and can be used as a basis for data-driven decision-making.]]>
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